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<class 'pandas.core.frame.DataFrame'> RangeIndex: 20 entries, 0 to 19 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Victim_Age 20 non-null object 1 Total_Victims 20 non-null float64 2 Male_Victims 20 non-null float64 3 Female_Victims 20 non-null float64 4 Unknown_Sex 20 non-null float64 5 White 20 non-null float64 6 Black_African_American 20 non-null float64 7 Other_Race 20 non-null float64 8 Unknown_Race 20 non-null float64 9 Hispanic_Latino 20 non-null float64 10 Not_Hispanic_Latino 20 non-null float64 11 Unknown_Ethnicity 20 non-null float64 dtypes: float64(11), object(1) memory usage: 2.0+ KB
Out[Â ]:
| 0 | |
|---|---|
| Victim_Age | 0 |
| Total_Victims | 0 |
| Male_Victims | 0 |
| Female_Victims | 0 |
| Unknown_Sex | 0 |
| White | 0 |
| Black_African_American | 0 |
| Other_Race | 0 |
| Unknown_Race | 0 |
| Hispanic_Latino | 0 |
| Not_Hispanic_Latino | 0 |
| Unknown_Ethnicity | 0 |
Out[Â ]:
| Victim_Age | Total_Victims | Male_Victims | Female_Victims | Unknown_Sex | White | Black_African_American | Other_Race | Unknown_Race | Hispanic_Latino | Not_Hispanic_Latino | Unknown_Ethnicity |
|---|
Out[Â ]:
| Victim_Age | Total_Victims | Male_Victims | Female_Victims | Unknown_Sex | White | Black_African_American | Other_Race | Unknown_Race | Hispanic_Latino | Not_Hispanic_Latino | Unknown_Ethnicity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | 60 to 64 | 611.0 | 424.0 | 186.0 | 1.0 | 355.0 | 209.0 | 28.0 | 19.0 | 45.0 | 395.0 | 68.0 |
| 16 | 65 to 69 | 417.0 | 284.0 | 133.0 | 0.0 | 252.0 | 136.0 | 24.0 | 5.0 | 32.0 | 276.0 | 29.0 |
| 17 | 70 to 74 | 245.0 | 139.0 | 105.0 | 1.0 | 162.0 | 62.0 | 17.0 | 4.0 | 13.0 | 169.0 | 15.0 |
| 18 | 75 and over | 305.0 | 147.0 | 156.0 | 2.0 | 228.0 | 47.0 | 20.0 | 10.0 | 17.0 | 199.0 | 16.0 |
| 19 | Unknown | 188.0 | 114.0 | 35.0 | 39.0 | 50.0 | 70.0 | 3.0 | 65.0 | 17.0 | 68.0 | 64.0 |
Out[Â ]:
| Total_Victims | Male_Victims | Female_Victims | Unknown_Sex | White | Black_African_American | Other_Race | Unknown_Race | Hispanic_Latino | Not_Hispanic_Latino | Unknown_Ethnicity | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.00000 | 20.00000 | 20.000000 |
| mean | 1076.000000 | 842.600000 | 229.050000 | 4.350000 | 430.600000 | 577.500000 | 34.000000 | 33.900000 | 166.60000 | 640.80000 | 87.100000 |
| std | 1020.181614 | 847.871229 | 177.521674 | 8.542618 | 346.084322 | 636.192415 | 27.545178 | 29.882049 | 184.43096 | 599.57341 | 77.799269 |
| min | 108.000000 | 64.000000 | 35.000000 | 0.000000 | 44.000000 | 39.000000 | 3.000000 | 4.000000 | 13.00000 | 66.00000 | 8.000000 |
| 25% | 234.500000 | 132.750000 | 100.500000 | 1.000000 | 142.500000 | 68.000000 | 15.250000 | 11.500000 | 18.50000 | 154.50000 | 22.000000 |
| 50% | 751.000000 | 578.000000 | 192.000000 | 3.000000 | 391.000000 | 331.500000 | 26.500000 | 26.500000 | 96.50000 | 461.00000 | 63.500000 |
| 75% | 1636.250000 | 1327.250000 | 372.000000 | 3.000000 | 715.250000 | 909.000000 | 55.250000 | 47.750000 | 292.75000 | 964.25000 | 134.500000 |
| max | 3807.000000 | 3063.000000 | 732.000000 | 39.000000 | 1323.000000 | 2266.000000 | 94.000000 | 124.000000 | 687.00000 | 2217.00000 | 304.000000 |
Out[Â ]:
array(['Under 24', 'Infant (under 1)', '1 to 4', '5 to 8', '9 to 12',
'13 to 16', '17 to 19', '20 to 24', '25 to 29', '30 to 34',
'35 to 39', '40 to 44', '45 to 49', '50 to 54', '55 to 59',
'60 to 64', '65 to 69', '70 to 74', '75 and over', 'Unknown'],
dtype=object)
Out[Â ]:
array(['Under 24', 'Infant (under 1)', '1 to 4', '5 to 8', '9 to 12',
'13 to 16', '17 to 19', '20 to 24', '25 to 29', '30 to 34',
'35 to 39', '40 to 44', '45 to 49', '50 to 54', '55 to 59',
'60 to 64', '65 to 69', '70 to 74', '75 and over', 'Unknown'],
dtype=object)
Analyzing Homicide Circumstances (Table 10)
Out[Â ]:
array(['Robbery', 'Burglary', 'Larceny-theft', 'Motor vehicle theft',
'Arson', 'Prostitution and commercialized vice',
'Other sex offenses', 'Narcotic drug laws', 'Gambling',
'Other-not specified ', 'Human trafficking/Commercial sex acts',
'Human trafficking/Involuntary servitude', 'Domestic violence',
'Child killed by babysitter', 'Brawl due to influence of alcohol',
'Brawl due to influence of narcotics',
'Argument over money or property', 'Other arguments',
'Gangland killings', 'Juvenile gang killings',
'Institutional killings', 'Sniper attack', 'Unknown'], dtype=object)
Deeper Analysis of Victim-Offender Relationships (Table 10)
Out[Â ]:
Index(['Circumstance', 'Total_Victims', 'Husband', 'Wife', 'Mother', 'Father',
'Son', 'Daughter', 'Brother', 'Sister', 'Other_Family', 'Boyfriend',
'Girlfriend', 'Neighbor', 'Employee', 'Employer', 'Stranger',
'Unknown'],
dtype='object')
Male vs. Female Victim Analysis
Out[Â ]:
array(['Robbery', 'Burglary', 'Larceny-theft', 'Motor vehicle theft',
'Arson', 'Prostitution and commercialized vice',
'Other sex offenses', 'Narcotic drug laws', 'Gambling',
'Other-not specified ', 'Human trafficking/Commercial sex acts',
'Human trafficking/Involuntary servitude', 'Domestic violence',
'Child killed by babysitter', 'Brawl due to influence of alcohol',
'Brawl due to influence of narcotics',
'Argument over money or property', 'Other arguments',
'Gangland killings', 'Juvenile gang killings',
'Institutional killings', 'Sniper attack', 'Unknown'], dtype=object)
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Analyzing Homicide Trends Over Time (2019-2023)
Out[Â ]:
Index(['Murder Circumstances, 2019–2023', 'Unnamed: 1', 'Unnamed: 2',
'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6'],
dtype='object')
Index(['Murder Circumstances, 2019–2023', 'Unnamed: 1', 'Unnamed: 2',
'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6'],
dtype='object')
Index(['Circumstance', 'Victims_2019', 'Victims_2020', 'Victims_2021',
'Victims_2022', 'Victims_2023'],
dtype='object')
Total Homicides by Year: [14404. 18857. 16633. 19939. 17713.]
[14404. 18857. 16633. 19939. 17713.]
Out[Â ]:
Index(['Circumstance', 'Victims_2019', 'Victims_2020', 'Victims_2021',
'Victims_2022', 'Victims_2023'],
dtype='object')
Out[Â ]:
array(['Circumstances', 'Total', 'Felony type total:', 'Rape', 'Robbery',
'Burglary', 'Larceny-theft', 'Motor vehicle theft', 'Arson',
'Prostitution and commercialized vice', 'Other sex offenses',
'Narcotic drug laws', 'Gambling', 'Other-not specified ',
'Human trafficking/Commercial sex acts',
'Human trafficking/Involuntary servitude',
'Suspected felony type1', 'Other than felony type total:',
'Domestic violence', 'Child killed by babysitter',
'Brawl due to influence of alcohol1',
'Brawl due to influence of narcotics1',
'Argument over money or property1', 'Other arguments',
'Gangland killings', 'Juvenile gang killings',
'Institutional killings', 'Sniper attack1', 'Unknown',
'1 Figures for suspected felony type, brawl due to influence of alcohol, brawl due to influence of narcotics, argument over money or property, and sniper attack include only data submitted by Summary reporting agencies because these circumstances are not collected via the National Incident-Based Reporting System.',
"NOTE: Prior years' crime data has been updated; therefore, data presented in this table may not match previously published data."],
dtype=object)
Analyzing Homicides by Weapon Type Over Time
Out[Â ]:
Index(['Murder Circumstances', 'Unnamed: 1', 'Unnamed: 2', ' ', 'Unnamed: 4',
'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9',
'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',
'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17',
'Unnamed: 18'],
dtype='object')
Found Yearly Columns: [] Valid Weapon Categories: []
Out[Â ]:
| Murder Circumstances | Unnamed: 1 | Unnamed: 2 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | by Weapon, 2023 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |||||||
| 1 | Circumstances | Total\nmurder\nvictims | Total\nfirearms | Handguns | Rifles | Shotguns | Other\nguns or\ntype not\nstated | Knives or\ncutting\ninstruments | Blunt\nobjects\n(clubs,\nhammers,\netc.) | Personal\nweapons\n(hands,\nfists, feet,\netc.) | Poison | Pushed\nor\nthrown\nout\nwindow1 | Explosives | Fire | Narcotics | Drowning1 | Strangulation1 | Asphyxiation | Other |
| 2 | Total | 17713 | 13529 | 7159 | 511 | 166 | 5693 | 1562 | 317 | 659 | 20 | 0 | 0 | 92 | 230 | 0 | 10 | 94 | 1200 |
| 3 | Felony type total: | 1321 | 907 | 522 | 24 | 11 | 350 | 85 | 27 | 35 | 1 | 0 | 0 | 39 | 130 | 0 | 4 | 7 | 86 |
| 4 | Rape | 10 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
['Circumstance', 'Total_Victims', 'Total_Firearms', 'Handguns', 'Rifles', 'Shotguns', 'Other_Guns', 'Knives', 'Blunt_Objects', 'Personal_Weapons', 'Poison', 'Pushed_Or_Thrown', 'Explosives', 'Fire', 'Narcotics', 'Drowning', 'Strangulation', 'Asphyxiation', 'Other']
Original columns: ['Circumstance', 'Total_Victims', 'Total_Firearms', 'Handguns', 'Rifles', 'Shotguns', 'Other_Guns', 'Knives', 'Blunt_Objects', 'Personal_Weapons', 'Poison', 'Pushed_Or_Thrown', 'Explosives', 'Fire', 'Narcotics', 'Drowning', 'Strangulation', 'Asphyxiation', 'Other'] Standardized columns: ['Circumstance', 'Total_Victims', 'Total_Firearms', 'Handguns', 'Rifles', 'Shotguns', 'Other_Guns', 'Knives', 'Blunt_Objects', 'Personal_Weapons', 'Poison', 'Pushed_Or_Thrown', 'Explosives', 'Fire', 'Narcotics', 'Drowning', 'Strangulation', 'Asphyxiation', 'Other']
Index(['Circumstance', 'Victims_2019', 'Victims_2020', 'Victims_2021',
'Victims_2022', 'Victims_2023'],
dtype='object')
Regional Breakdown
Index(['Region', 'Total_Weapons', 'Firearms_Percent', 'Knives_Percent',
'Other_Weapons_Percent', 'Personal_Weapons_Percent'],
dtype='object')
Table 3
Index(['Offender_Age', 'Total_Offenders', 'Male_Offenders', 'Female_Offenders',
'Unknown_Sex', 'White', 'Black_African_American', 'Other_Race',
'Unknown_Race', 'Hispanic_Latino', 'Not_Hispanic_Latino',
'Unknown_Ethnicity'],
dtype='object')
Male vs. Female Offenders
Index(['Offender_Age', 'Total_Offenders', 'Male_Offenders', 'Female_Offenders',
'Unknown_Sex', 'White', 'Black_African_American', 'Other_Race',
'Unknown_Race', 'Hispanic_Latino', 'Not_Hispanic_Latino',
'Unknown_Ethnicity'],
dtype='object')
Table 4: Victim/Offender Situations
Index(['Victim_Offender_Situation', 'Total_Cases'], dtype='object')
Victim_Offender_Situation Total_Cases
0 Total 17713.0
1 Single victim/single offender 9037.0
2 Single victim/unknown offender or offenders 3497.0
3 Single victim/multiple offenders 2816.0
4 Multiple victims/single offender 1356.0
['Total' 'Single victim/single offender' 'Single victim/unknown offender or offenders' 'Single victim/multiple offenders' 'Multiple victims/single offender' 'Multiple victims/multiple offenders' 'Multiple victims/unknown offender or offenders' '1 Because of rounding, the percentages may not add to 100.0.']
Table 5: Victim Vs. Offender Age Comparison
Index(['Victim_Age', 'Total_Victims', 'Offender_Under_18',
'Offender_18_and_Over', 'Offender_Unknown'],
dtype='object')
Victim_Age Total_Victims \
0 Unknown 55.0
1 NOTE: This table is based on incidents where ... NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
Offender_Under_18 Offender_18_and_Over Offender_Unknown
0 0.0 47.0 8.0
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
Table 6: Single Victom/Single Offender Cases
Index(['Victim_Race', 'Total_Cases', 'Offender_White',
'Offender_Black_African_American', 'Offender_Other', 'Offender_Unknown',
'Offender_Male', 'Offender_Female', 'Offender_Unknown_Sex',
'Offender_Hispanic_Latino', 'Offender_Not_Hispanic_Latino',
'Offender_Unknown_Ethnicity'],
dtype='object')
Victim_Race Total_Cases Offender_White \
0 White 4011.0 3102.0
1 Black or African American 3823.0 324.0
2 Other race1 292.0 85.0
3 Unknown race 235.0 80.0
4 Sex of victim NaN NaN
Offender_Black_African_American Offender_Other Offender_Unknown \
0 726.0 71.0 112.0
1 3282.0 18.0 199.0
2 51.0 147.0 9.0
3 76.0 13.0 66.0
4 NaN NaN NaN
Offender_Male Offender_Female Offender_Unknown_Sex \
0 3606.0 380.0 25.0
1 3319.0 392.0 112.0
2 260.0 30.0 2.0
3 213.0 16.0 6.0
4 NaN NaN NaN
Offender_Hispanic_Latino Offender_Not_Hispanic_Latino \
0 866.0 1804.0
1 132.0 2453.0
2 29.0 147.0
3 58.0 84.0
4 NaN NaN
Offender_Unknown_Ethnicity
0 426.0
1 663.0
2 43.0
3 49.0
4 NaN
Female Offender Breakdown by race
Total_Cases Offender_White Offender_Black_African_American \
count 10.000000 10.000000 10.000000
mean 2372.400000 1014.000000 1180.500000
std 2220.741328 1090.024974 1388.703572
min 22.000000 9.000000 10.000000
25% 395.250000 134.750000 112.250000
50% 1740.500000 592.000000 537.500000
75% 3964.000000 1659.500000 2459.000000
max 6042.000000 3102.000000 3282.000000
Offender_Other Offender_Unknown Offender_Male Offender_Female \
count 10.000000 10.000000 10.000000 10.000000
mean 68.900000 109.000000 2100.100000 231.200000
std 61.688194 97.779798 1953.467484 226.027432
min 2.000000 1.000000 18.000000 3.000000
25% 18.500000 48.750000 351.000000 39.500000
50% 50.000000 78.000000 1573.000000 149.000000
75% 128.250000 172.000000 3534.250000 389.000000
max 160.000000 312.000000 5335.000000 589.000000
Offender_Unknown_Sex Offender_Hispanic_Latino \
count 10.000000 10.000000
mean 41.100000 321.400000
std 47.880523 342.106611
min 1.000000 4.000000
25% 6.750000 52.000000
50% 20.000000 204.500000
75% 79.250000 606.000000
max 118.000000 866.000000
Offender_Not_Hispanic_Latino Offender_Unknown_Ethnicity
count 10.000000 10.000000
mean 1330.400000 339.600000
std 1407.724815 308.546845
min 8.000000 4.000000
25% 178.000000 60.750000
50% 780.000000 300.000000
75% 2290.750000 561.000000
max 3757.000000 909.000000
Offender_White 10140.0
Offender_Black_African_American 11805.0
Offender_Other 689.0
Offender_Unknown 1090.0
dtype: float64
Total_Cases Offender_White \
Victim_Race
White 4011.0 3102.0
Black or African American 3823.0 324.0
Male 6042.0 2386.0
Not Hispanic or Latino 5113.0 1814.0
Offender_Black_African_American Offender_Other \
Victim_Race
White 726.0 71.0
Black or African American 3282.0 18.0
Male 3184.0 160.0
Not Hispanic or Latino 2965.0 142.0
Offender_Unknown Offender_Male Offender_Female \
Victim_Race
White 112.0 3606.0 380.0
Black or African American 199.0 3319.0 392.0
Male 312.0 5335.0 589.0
Not Hispanic or Latino 192.0 4480.0 536.0
Offender_Unknown_Sex Offender_Hispanic_Latino \
Victim_Race
White 25.0 866.0
Black or African American 112.0 132.0
Male 118.0 804.0
Not Hispanic or Latino 97.0 279.0
Offender_Not_Hispanic_Latino \
Victim_Race
White 1804.0
Black or African American 2453.0
Male 3220.0
Not Hispanic or Latino 3757.0
Offender_Unknown_Ethnicity
Victim_Race
White 426.0
Black or African American 663.0
Male 909.0
Not Hispanic or Latino 606.0
Most common victim-offender combinations:
Total_Cases Offender_White \
Victim_Race
Male 6042.0 2386.0
Not Hispanic or Latino 5113.0 1814.0
White 4011.0 3102.0
Black or African American 3823.0 324.0
Female 2297.0 1196.0
Offender_Black_African_American Offender_Other \
Victim_Race
Male 3184.0 160.0
Not Hispanic or Latino 2965.0 142.0
White 726.0 71.0
Black or African American 3282.0 18.0
Female 941.0 87.0
Offender_Unknown Offender_Male Offender_Female \
Victim_Race
Male 312.0 5335.0 589.0
Not Hispanic or Latino 192.0 4480.0 536.0
White 112.0 3606.0 380.0
Black or African American 199.0 3319.0 392.0
Female 73.0 2045.0 226.0
Offender_Unknown_Sex Offender_Hispanic_Latino \
Victim_Race
Male 118.0 804.0
Not Hispanic or Latino 97.0 279.0
White 25.0 866.0
Black or African American 112.0 132.0
Female 26.0 277.0
Offender_Not_Hispanic_Latino \
Victim_Race
Male 3220.0
Not Hispanic or Latino 3757.0
White 1804.0
Black or African American 2453.0
Female 1260.0
Offender_Unknown_Ethnicity
Victim_Race
Male 909.0
Not Hispanic or Latino 606.0
White 426.0
Black or African American 663.0
Female 268.0
Least common victim-offender combinations:
Total_Cases Offender_White \
Victim_Race
Other race1 292.0 85.0
Unknown race 235.0 80.0
Unknown sex 22.0 9.0
Sex of victim NaN NaN
Ethnicity of victim NaN NaN
Offender_Black_African_American Offender_Other \
Victim_Race
Other race1 51.0 147.0
Unknown race 76.0 13.0
Unknown sex 10.0 2.0
Sex of victim NaN NaN
Ethnicity of victim NaN NaN
Offender_Unknown Offender_Male Offender_Female \
Victim_Race
Other race1 9.0 260.0 30.0
Unknown race 66.0 213.0 16.0
Unknown sex 1.0 18.0 3.0
Sex of victim NaN NaN NaN
Ethnicity of victim NaN NaN NaN
Offender_Unknown_Sex Offender_Hispanic_Latino \
Victim_Race
Other race1 2.0 29.0
Unknown race 6.0 58.0
Unknown sex 1.0 4.0
Sex of victim NaN NaN
Ethnicity of victim NaN NaN
Offender_Not_Hispanic_Latino Offender_Unknown_Ethnicity
Victim_Race
Other race1 147.0 43.0
Unknown race 84.0 49.0
Unknown sex 8.0 4.0
Sex of victim NaN NaN
Ethnicity of victim NaN NaN
Offender_Male 21001.0 Offender_Female 2312.0 Offender_Unknown_Sex 411.0 dtype: float64 Male-to-Female Offender Ratio: 9.08
Index(['Total_Cases', 'Offender_White', 'Offender_Black_African_American',
'Offender_Other', 'Offender_Unknown', 'Offender_Male',
'Offender_Female', 'Offender_Unknown_Sex', 'Offender_Hispanic_Latino',
'Offender_Not_Hispanic_Latino', 'Offender_Unknown_Ethnicity'],
dtype='object')
Analyzing Homicide Circumstances by Relationship
Index(['Circumstance', 'Total_Victims', 'Husband', 'Wife', 'Mother', 'Father',
'Son', 'Daughter', 'Brother', 'Sister', 'Other_Family', 'Boyfriend',
'Girlfriend', 'Neighbor', 'Employee', 'Employer', 'Stranger',
'Unknown'],
dtype='object')
Comparing Family vs. Non-Family Homicide Trends Over Time
Index(['Circumstance', 'Victims_2019', 'Victims_2020', 'Victims_2021',
'Victims_2022', 'Victims_2023'],
dtype='object')
Comparing Homicide Circumstances
['Circumstances' 'Total' 'Felony type total:' 'Rape' 'Robbery' 'Burglary' 'Larceny-theft' 'Motor vehicle theft' 'Arson' 'Prostitution and commercialized vice' 'Other sex offenses' 'Narcotic drug laws' 'Gambling' 'Other-not specified ' 'Human trafficking/Commercial sex acts' 'Human trafficking/Involuntary servitude' 'Suspected felony type1' 'Other than felony type total:' 'Domestic violence' 'Child killed by babysitter' 'Brawl due to influence of alcohol1' 'Brawl due to influence of narcotics1' 'Argument over money or property1' 'Other arguments' 'Gangland killings' 'Juvenile gang killings' 'Institutional killings' 'Sniper attack1' 'Unknown' '1 Figures for suspected felony type, brawl due to influence of alcohol, brawl due to influence of narcotics, argument over money or property, and sniper attack include only data submitted by Summary reporting agencies because these circumstances are not collected via the National Incident-Based Reporting System.' "NOTE: Prior years' crime data has been updated; therefore, data presented in this table may not match previously published data."]
Table 8: Murder Victims by Weapons
File saved successfully!
Out[Â ]:
| Weapon_Type | Victims_2019 | Victims_2020 | Victims_2021 | Victims_2022 | Victims_2023 | |
|---|---|---|---|---|---|---|
| 0 | Weapons | 2019.0 | 2020.0 | 2021.0 | 2022.0 | 2023.0 |
| 1 | Handguns | 6544.0 | 8629.0 | 6720.0 | 8223.0 | 7159.0 |
| 2 | Rifles | 367.0 | 486.0 | 467.0 | 556.0 | 511.0 |
| 3 | Shotguns | 210.0 | 213.0 | 171.0 | 188.0 | 166.0 |
| 4 | Other guns | 48.0 | 108.0 | 299.0 | 431.0 | 398.0 |
Out[Â ]:
| Weapon_Type | Victims_2019 | Victims_2020 | Victims_2021 | Victims_2022 | Victims_2023 | |
|---|---|---|---|---|---|---|
| 0 | Handguns | 6544.0 | 8629.0 | 6720.0 | 8223.0 | 7159.0 |
| 1 | Rifles | 367.0 | 486.0 | 467.0 | 556.0 | 511.0 |
| 2 | Shotguns | 210.0 | 213.0 | 171.0 | 188.0 | 166.0 |
| 3 | Other guns | 48.0 | 108.0 | 299.0 | 431.0 | 398.0 |
| 4 | Firearms, type not stated | 3357.0 | 4991.0 | 5330.0 | 5846.0 | 5295.0 |
Index(['Weapon_Type', 'Victims_2019', 'Victims_2020', 'Victims_2021',
'Victims_2022', 'Victims_2023'],
dtype='object')
<ipython-input-97-97cde585f958>:2: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values. df8_pct_change = df8.pct_change(axis=1) * 100
Table 9: Murder Victims by Age and Weapon
Out[Â ]:
['.config', 'Cleaned_Table_8.csv', 'drive', 'sample_data']
Out[Â ]:
| Age_Group | Total_Victims | Firearms | Knives | Blunt_Objects | Personal_Weapons | Poison | Explosives | Fire | Narcotics | Strangulation | Asphyxiation | Other_Weapons | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | Total | 17713.0 | 13529.0 | 1562.0 | 317.0 | 659.0 | 20.0 | 0.0 | 92.0 | 230.0 | 10.0 | 94.0 | 1200.0 |
| 2 | Percent distribution4 | 100.0 | 76.4 | 8.8 | 1.8 | 3.7 | 0.1 | 0.0 | 0.5 | 1.3 | 0.1 | 0.5 | 6.8 |
| 3 | Under 185 | 1690.0 | 1207.0 | 69.0 | 15.0 | 151.0 | 6.0 | 0.0 | 8.0 | 41.0 | 4.0 | 21.0 | 168.0 |
| 4 | Under 225 | 3807.0 | 3096.0 | 154.0 | 21.0 | 167.0 | 6.0 | 0.0 | 11.0 | 54.0 | 4.0 | 22.0 | 272.0 |
Out[Â ]:
| Victim_Age | Total_Victims | Firearms | Knives | Blunt_Objects | Personal_Weapons | Poison | Explosives | Fire | Narcotics | Strangulation | Asphyxiation | Other | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Under 225 | 3807.0 | 3096.0 | 154.0 | 21.0 | 167.0 | 6.0 | 0.0 | 11.0 | 54.0 | 4.0 | 22.0 | 272.0 |
| 1 | 18 and over5 | 15835.0 | 12219.0 | 1480.0 | 300.0 | 489.0 | 13.0 | 0.0 | 82.0 | 187.0 | 5.0 | 72.0 | 988.0 |
| 2 | Infant (under 1) | 139.0 | 27.0 | 2.0 | 3.0 | 55.0 | 0.0 | 0.0 | 0.0 | 7.0 | 0.0 | 9.0 | 36.0 |
| 3 | 1 to 4 | 203.0 | 56.0 | 5.0 | 5.0 | 65.0 | 3.0 | 0.0 | 6.0 | 15.0 | 0.0 | 6.0 | 42.0 |
| 4 | 5 to 8 | 108.0 | 53.0 | 8.0 | 3.0 | 12.0 | 1.0 | 0.0 | 2.0 | 1.0 | 1.0 | 5.0 | 22.0 |
Out[Â ]:
| Victim_Age | Total_Victims | Firearms | Knives | Blunt_Objects | Personal_Weapons | Poison | Explosives | Fire | Narcotics | Strangulation | Asphyxiation | Other | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Under 225 | 3807.0 | 3096.0 | 154.0 | 21.0 | 167.0 | 6.0 | 0.0 | 11.0 | 54.0 | 4.0 | 22.0 | 272.0 |
| 1 | 18 and over5 | 15835.0 | 12219.0 | 1480.0 | 300.0 | 489.0 | 13.0 | 0.0 | 82.0 | 187.0 | 5.0 | 72.0 | 988.0 |
| 2 | Infant (under 1) | 139.0 | 27.0 | 2.0 | 3.0 | 55.0 | 0.0 | 0.0 | 0.0 | 7.0 | 0.0 | 9.0 | 36.0 |
| 3 | 1 to 4 | 203.0 | 56.0 | 5.0 | 5.0 | 65.0 | 3.0 | 0.0 | 6.0 | 15.0 | 0.0 | 6.0 | 42.0 |
| 4 | 5 to 8 | 108.0 | 53.0 | 8.0 | 3.0 | 12.0 | 1.0 | 0.0 | 2.0 | 1.0 | 1.0 | 5.0 | 22.0 |
<ipython-input-108-18b1ec3d2a40>:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df9[col] = pd.to_numeric(df9[col], errors="coerce")
Out[Â ]:
| 0 | |
|---|---|
| Victim_Age | object |
| Total_Victims | float64 |
| Firearms | float64 |
| Knives | float64 |
| Blunt_Objects | float64 |
| Personal_Weapons | float64 |
| Poison | float64 |
| Explosives | float64 |
| Fire | float64 |
| Narcotics | float64 |
| Strangulation | float64 |
| Asphyxiation | float64 |
| Other | float64 |
Out[Â ]:
['.config', 'Cleaned_Table_9.csv', 'Cleaned_Table_8.csv', 'drive', 'sample_data']
Victim_Age Total_Victims Firearms Knives Blunt_Objects \ 0 Under 225 3807.0 3096.0 154.0 21.0 1 18 and over5 15835.0 12219.0 1480.0 300.0 2 Infant (under 1) 139.0 27.0 2.0 3.0 3 1 to 4 203.0 56.0 5.0 5.0 4 5 to 8 108.0 53.0 8.0 3.0 Personal_Weapons Poison Explosives Fire Narcotics Strangulation \ 0 167.0 6.0 0.0 11.0 54.0 4.0 1 489.0 13.0 0.0 82.0 187.0 5.0 2 55.0 0.0 0.0 0.0 7.0 0.0 3 65.0 3.0 0.0 6.0 15.0 0.0 4 12.0 1.0 0.0 2.0 1.0 1.0 Asphyxiation Other 0 22.0 272.0 1 72.0 988.0 2 9.0 36.0 3 6.0 42.0 4 5.0 22.0 Firearm Deaths: 28844.0 Non-Firearm Deaths: 8511.0
Table 11: Murder circumstances by Weapon
Out[Â ]:
| Murder Circumstances | Unnamed: 1 | Unnamed: 2 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | by Weapon, 2023 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |||||||
| 1 | Circumstances | Total\nmurder\nvictims | Total\nfirearms | Handguns | Rifles | Shotguns | Other\nguns or\ntype not\nstated | Knives or\ncutting\ninstruments | Blunt\nobjects\n(clubs,\nhammers,\netc.) | Personal\nweapons\n(hands,\nfists, feet,\netc.) | Poison | Pushed\nor\nthrown\nout\nwindow1 | Explosives | Fire | Narcotics | Drowning1 | Strangulation1 | Asphyxiation | Other |
| 2 | Total | 17713 | 13529 | 7159 | 511 | 166 | 5693 | 1562 | 317 | 659 | 20 | 0 | 0 | 92 | 230 | 0 | 10 | 94 | 1200 |
| 3 | Felony type total: | 1321 | 907 | 522 | 24 | 11 | 350 | 85 | 27 | 35 | 1 | 0 | 0 | 39 | 130 | 0 | 4 | 7 | 86 |
| 4 | Rape | 10 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 34 entries, 0 to 33 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Murder Circumstances 34 non-null object 1 Unnamed: 1 31 non-null object 2 Unnamed: 2 31 non-null object 3 30 non-null object 4 Unnamed: 4 30 non-null object 5 Unnamed: 5 31 non-null object 6 Unnamed: 6 31 non-null object 7 Unnamed: 7 31 non-null object 8 Unnamed: 8 30 non-null object 9 Unnamed: 9 30 non-null object 10 Unnamed: 10 31 non-null object 11 Unnamed: 11 31 non-null object 12 Unnamed: 12 30 non-null object 13 Unnamed: 13 30 non-null object 14 Unnamed: 14 30 non-null object 15 Unnamed: 15 30 non-null object 16 Unnamed: 16 30 non-null object 17 Unnamed: 17 30 non-null object 18 Unnamed: 18 30 non-null object dtypes: object(19) memory usage: 5.2+ KB
Out[Â ]:
| Murder Circumstances | Unnamed: 1 | Unnamed: 2 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | by Weapon, 2023 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |||||||
| 1 | Circumstances | Total\nmurder\nvictims | Total\nfirearms | Handguns | Rifles | Shotguns | Other\nguns or\ntype not\nstated | Knives or\ncutting\ninstruments | Blunt\nobjects\n(clubs,\nhammers,\netc.) | Personal\nweapons\n(hands,\nfists, feet,\netc.) | Poison | Pushed\nor\nthrown\nout\nwindow1 | Explosives | Fire | Narcotics | Drowning1 | Strangulation1 | Asphyxiation | Other |
| 2 | Total | 17713 | 13529 | 7159 | 511 | 166 | 5693 | 1562 | 317 | 659 | 20 | 0 | 0 | 92 | 230 | 0 | 10 | 94 | 1200 |
| 3 | Felony type total: | 1321 | 907 | 522 | 24 | 11 | 350 | 85 | 27 | 35 | 1 | 0 | 0 | 39 | 130 | 0 | 4 | 7 | 86 |
| 4 | Rape | 10 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
Number of columns in df11: 19 New columns list length: 19
Standardized columns: ['Murder Circumstances', 'Unnamed: 1', 'Unnamed: 2', '', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18']
Renamed columns: ['Circumstance', 'Total_Victims', 'Total_Firearms', 'Handguns', 'Rifles', 'Shotguns', 'Other_Guns', 'Knives', 'Blunt_Objects', 'Personal_Weapons', 'Poison', 'Pushed_Or_Thrown', 'Explosives', 'Fire', 'Narcotics', 'Drowning', 'Strangulation', 'Asphyxiation', 'Other']
Index(['Circumstance', 'Total_Victims', 'Total_Firearms', 'Handguns', 'Rifles',
'Shotguns', 'Other_Guns', 'Knives', 'Blunt_Objects', 'Personal_Weapons',
'Poison', 'Pushed_Or_Thrown', 'Explosives', 'Fire', 'Narcotics',
'Drowning', 'Strangulation', 'Asphyxiation', 'Other'],
dtype='object')
Out[Â ]:
| Murder Circumstances, 2019–2023 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | |
|---|---|---|---|---|---|---|---|
| 0 | Circumstances | 2019.0 | 2020.0 | 2021.0 | 2022.0 | 2023.0 | NaN |
| 1 | Total | 14404.0 | 18857.0 | 16633.0 | 19939.0 | 17713.0 | NaN |
| 2 | Felony type total: | 2081.0 | 2175.0 | 1287.0 | 1633.0 | 1321.0 | NaN |
| 3 | Rape | 12.0 | 20.0 | 8.0 | 25.0 | 10.0 | NaN |
| 4 | Robbery | 520.0 | 565.0 | 263.0 | 397.0 | 312.0 | NaN |
['Murder Circumstances, 2019–2023', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6']
['Circumstance', 'Victims_2019', 'Victims_2020', 'Victims_2021', 'Victims_2022', 'Victims_2023']
Total Homicides by Year: [14404. 18857. 16633. 19939. 17713.]
Justifiable Homicide Unnamed: 1 Unnamed: 2 \
0 by Weapon, Law Enforcement,1 2019–2023 NaN NaN
1 Year Total Total\nfirearms
2 2019 380 372
3 2020 338 331
4 2021 220 214
Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6 \
0 NaN NaN NaN NaN
1 Handguns Rifles Shotguns Firearms,\ntype not\nstated
2 273 36 3 60
3 223 36 2 70
4 149 30 0 35
Unnamed: 7 Unnamed: 8 \
0 NaN NaN
1 Knives or\ncutting\ninstruments Other\ndangerous\nweapons
2 2 3
3 1 4
4 1 4
Unnamed: 9
0 NaN
1 Personal\nweapons
2 3
3 2
4 1
['Justifiable Homicide', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9']
Renamed columns: ['Justifiable_Homicide', 'Victims_2019', 'Victims_2020', 'Victims_2021', 'Victims_2022', 'Victims_2023']
Original columns: ['Justifiable Homicide', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9'] Renamed columns: ['Justifiable_Homicide', 'Victims_2019', 'Victims_2020', 'Victims_2021', 'Victims_2022', 'Victims_2023']
Victims_2019 Victims_2020 Victims_2021 Victims_2022 Victims_2023 0 NaN NaN NaN NaN NaN 1 NaN NaN NaN NaN NaN 2 380.0 372.0 273.0 36.0 3.0 3 338.0 331.0 223.0 36.0 2.0 4 220.0 214.0 149.0 30.0 0.0 Victims_2019 float64 Victims_2020 float64 Victims_2021 float64 Victims_2022 float64 Victims_2023 float64 dtype: object
Unique values in Justifiable_Homicide: ['by Weapon, Law Enforcement,1 2019–2023' 'Year' '2019' '2020' '2021' '2022' '2023' '1 The killing of a felon by a law enforcement officer in the line of duty.' "NOTE: Prior years' crime data has been updated; therefore, data presented in this table may not match previously published data."] Filtered DataFrame: Justifiable_Homicide Victims_2019 Victims_2020 Victims_2021 \ 2 2019 380.0 372.0 273.0 3 2020 338.0 331.0 223.0 4 2021 220.0 214.0 149.0 5 2022 354.0 342.0 232.0 6 2023 303.0 296.0 195.0 Victims_2022 Victims_2023 2 36.0 3.0 3 36.0 2.0 4 30.0 0.0 5 38.0 1.0 6 32.0 2.0
Before filtering: ['by Weapon, Law Enforcement,1 2019–2023' 'Year' '2019' '2020' '2021' '2022' '2023' '1 The killing of a felon by a law enforcement officer in the line of duty.' "NOTE: Prior years' crime data has been updated; therefore, data presented in this table may not match previously published data."] After filtering: ['2019' '2020' '2021' '2022' '2023']
Justifiable_Homicide Victims_2019 \ 0 by Weapon, Law Enforcement,1 2019–2023 NaN 1 Year NaN 2 2019 380.0 3 2020 338.0 4 2021 220.0 5 2022 354.0 6 2023 303.0 7 1 The killing of a felon by a law enforcement ... NaN 8 NOTE: Prior years' crime data has been updated... NaN Victims_2020 Victims_2021 Victims_2022 Victims_2023 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 372.0 273.0 36.0 3.0 3 331.0 223.0 36.0 2.0 4 214.0 149.0 30.0 0.0 5 342.0 232.0 38.0 1.0 6 296.0 195.0 32.0 2.0 7 NaN NaN NaN NaN 8 NaN NaN NaN NaN
Justifiable_Homicide Victims_2019 Victims_2020 Victims_2021 \ 0 Year NaN NaN NaN 1 2019 380.0 372.0 273.0 2 2020 338.0 331.0 223.0 3 2021 220.0 214.0 149.0 4 2022 354.0 342.0 232.0 5 2023 303.0 296.0 195.0 Victims_2022 Victims_2023 0 NaN NaN 1 36.0 3.0 2 36.0 2.0 3 30.0 0.0 4 38.0 1.0 5 32.0 2.0
Original columns: ['Justifiable Homicide', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9']
Renamed columns: ['Justifiable_Homicide', 'Victims_2019', 'Victims_2020', 'Victims_2021', 'Victims_2022', 'Victims_2023']
['Justifiable_Homicide', 'Victims_2019', 'Victims_2020', 'Victims_2021', 'Victims_2022', 'Victims_2023']
<ipython-input-155-754f4aa8ca8b>:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df15[col] = pd.to_numeric(df15[col], errors="coerce")
Unique Justifiable Homicide entries after filtering: ['by weapon, private citizen,1 2019–2023' 'year' '2019' '2020' '2021' '2022' '2023']
Justifiable_Homicide 0 by weapon, private citizen,1 2019–2023 1 year 2 2019 3 2020 4 2021 5 2022 6 2023 7 1 the killing of a felon, during the commission of a felony, by a private citizen. 8 note: prior years' crime data has been updated; therefore, data presented in this table may not match previously published data.
